fix: incorrect linking

notebooks/official/automl/README.md

@@ -43,7 +43,7 @@ The steps performed include:
 
 [AutoML training text entity extraction model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb)
 
-In this tutorial, you how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK.
+Learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK.
 
 The steps performed include:
 

notebooks/official/explainable_ai/README.md

@@ -1,7 +1,7 @@
 
 [Custom training tabular regression model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)
 
-In this tutorial, you how learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
+Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
 
 The steps performed include:
 
@@ -29,7 +29,7 @@ The steps performed include:
 
 [AutoML training tabular classification model for online explanation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb)
 
-In this tutorial, you how to learn to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Online Prediction` to make online predictions with explanations.
+Learn how to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Online Prediction` to make online predictions with explanations.
 
 The steps performed include:
 

notebooks/official/feature_store/README.md

@@ -1,5 +1,5 @@
 
-[Using Vertex AI Feature Store with pandas DataFrame](official/feature_store/sdk-feature-store-pandas.ipynb)
+[Using Vertex AI Feature Store with pandas DataFrame](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb)
 
 Learn how to use `Vertex AI Feature Store` with pandas DataFrame.
 
@@ -13,7 +13,7 @@ The steps performed include:
 - Online serving with updated feature values.
 - Point-in-time correctness to fetch feature values for training.
 
-[Online and Batch predictions using Vertex AI Feature Store](official/feature_store/sdk-feature-store.ipynb)
+[Online and Batch predictions using Vertex AI Feature Store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store.ipynb)
 
 Learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.
 

notebooks/official/matching_engine/README.md

@@ -1,5 +1,5 @@
 
-[Create Vertex AI Matching Engine index](official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
+[Create Vertex AI Matching Engine index](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
 
 Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index.
 
@@ -12,7 +12,7 @@ The steps performed include:
 * Compute recall
 
 
-[Introduction to builtin Swivel embedding algorithm](official/matching_engine/intro-swivel.ipynb)
+[Introduction to builtin Swivel embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/intro-swivel.ipynb)
 
 Learn how to train custom embeddings using Vertex AI Pipelines and deploy the model for serving.
 
@@ -25,7 +25,7 @@ The steps performed include:
 5. **Predict**: Calling the deployed endpoint using online prediction.
 6. **Cleaning up**: Deleting resources created by this tutorial.
 
-[Introduction to builtin Two-towers embedding algorithm](official/matching_engine/two-tower-model-introduction.ipynb)
+[Introduction to builtin Two-towers embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/two-tower-model-introduction.ipynb)
 
 Learn how to run the two-tower model.
 

notebooks/official/ml_metadata/README.md

@@ -1,5 +1,5 @@
 
-[Track parameters and metrics for locally trained models](official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
+[Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
 
 Learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
 
@@ -8,7 +8,7 @@ The steps performed include:
 - Track parameters and metrics for a locally trained model.
 - Extract and perform analysis for all parameters and metrics within an Experiment.
 
-[Track parameters and metrics for custom training jobs](official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
+[Track parameters and metrics for custom training jobs](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
 
 Learn how to use Vertex AI SDK for Python to:
 
@@ -16,7 +16,7 @@ The steps performed include:
 - Track training parameters and prediction metrics for a custom training job.
 - Extract and perform analysis for all parameters and metrics within an Experiment.
 
-[Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata](official/ml_metadata/vertex-pipelines-ml-metadata.ipynb)
+[Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb)
 
 Learn how to track artifacts and metrics with `Vertex ML Metadata` in `Vertex AI Pipeline` runs.
 

notebooks/official/model-registry/README.md

@@ -1,5 +1,5 @@
 
-[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](official/model-registry/bqml-vertexai-model-registry.ipynb)
+[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model-registry/bqml-vertexai-model-registry.ipynb)
 
 Learn how to use `Vertex AI Model Registry` with `BigQuery ML` and make batch predictions:
 

notebooks/official/model_evaluation/README.md

@@ -1,5 +1,5 @@
 
-[Evaluating BatchPrediction results from AutoML Tabular Classification model](official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb)
+[Evaluating BatchPrediction results from AutoML Tabular Classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb)
 
 Learn how to train a Vertex AI AutoML Tabular Classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
 
@@ -12,7 +12,7 @@ The steps performed include:
 - Evaulate the AutoML model using the `Classification Evaluation Component`.
 - Import the classification metrics to the AutoML model resource.
 
-[Evaluating BatchPrediction results from AutoML Tabular Classification model](official/model_evaluation/automl_video_classification_model_evaluation.ipynb)
+[Evaluating BatchPrediction results from AutoML Tabular Classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb)
 
 Learn how to train a Vertex AI AutoML Tabular Classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
 
@@ -25,7 +25,7 @@ The steps performed include:
 - Evaulate the AutoML model using the `Classification Evaluation Component`.
 - Import the classification metrics to the AutoML model resource.
 
-[Evaluating BatchPrediction results from AutoML Tabular regression model](official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb)
+[Evaluating BatchPrediction results from AutoML Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb)
 
 Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
 
@@ -39,7 +39,7 @@ The steps performed include:
 - Evaulate the AutoML model using the `regression evaluation component`
 - Import the Classification Metrics to the AutoML model resource
 
-[Evaluating Batch Prediction results from Custom Tabular regression model](official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)
+[Evaluating Batch Prediction results from Custom Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)
 
 Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
 
@@ -55,7 +55,7 @@ The steps performed include:
 - Evaulate the model using the `regression evaluation component`
 - Import the Classification Metrics to the Vertex AI model resource
 
-[AutoML text classification pipelines using google-cloud-pipeline-components](official/model_evaluation/automl_text_classification_model_evaluation.ipynb)
+[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_text_classification_model_evaluation.ipynb)
 
 Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.
 

notebooks/official/model_monitoring/README.md

@@ -1,5 +1,5 @@
 
-[Vertex AI Model Monitoring with Explainable AI Feature Attributions](official/model_monitoring/model_monitoring.ipynb)
+[Vertex AI Model Monitoring with Explainable AI Feature Attributions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb)
 
 Learn to use the `Vertex AI Model Monitoring` service to detect drift and anomalies in prediction requests from a deployed `Vertex AI Model` resource.
 

notebooks/official/pipelines/README.md

@@ -1,5 +1,5 @@
 
-[AutoML image classification pipelines using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
+[AutoML image classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
 
 Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` image classification model.
 
@@ -15,7 +15,7 @@ The steps performed include:
 
 
 
-[Metrics visualization and run comparison using the KFP SDK](official/pipelines/metrics_viz_run_compare_kfp.ipynb)
+[Metrics visualization and run comparison using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb)
 
 Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
 
@@ -28,7 +28,7 @@ The steps performed include:
 - Execute KFP pipelines
 - Compare metrics across pipeline runs
 
-[Lightweight Python function-based components, and component I/O](official/pipelines/lightweight_functions_component_io_kfp.ipynb)
+[Lightweight Python function-based components, and component I/O](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
 
 Learn to use the KFP SDK to build lightweight Python function-based components, and then you learn to use `Vertex AI Pipelines` to execute the pipeline.
 
@@ -41,7 +41,7 @@ The steps performed include:
 - Compile the KFP pipeline.
 - Execute the KFP pipeline using `Vertex AI Pipelines`
 
-[Custom training with pre-built Google Cloud Pipeline Components](official/pipelines/custom_model_training_and_batch_prediction.ipynb)
+[Custom training with pre-built Google Cloud Pipeline Components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)
 
 Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build a custom model.
 
@@ -56,7 +56,7 @@ The steps performed include:
 
 
 
-[AutoML Tabular pipelines using google-cloud-pipeline-components](official/pipelines/automl_tabular_classification_beans.ipynb)
+[AutoML Tabular pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)
 
 Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular classification model.
 
@@ -72,7 +72,7 @@ The steps performed include:
 
 
 
-[Training an acquisition-prediction model using Swivel, BigQuery ML and Vertex AI Pipelines](official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb)
+[Training an acquisition-prediction model using Swivel, BigQuery ML and Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb)
 
 Learn how to build a simple BigQuery ML pipeline using Vertex AI pipelines in order to calculate text embeddings of content from articles and classify them
 into the *corporate acquisitions* category.
@@ -85,7 +85,7 @@ The steps performed include:
 - Building and configuring a Kubeflow DSL pipeline with all the created components.
 - Compiling and running the pipeline in Vertex AI Pipelines.
 
-[Pipelines introduction for KFP](official/pipelines/pipelines_intro_kfp.ipynb)
+[Pipelines introduction for KFP](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
 
 Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
 
@@ -94,7 +94,7 @@ The steps performed include:
 - Define and compile a `Vertex AI` pipeline.
 - Specify which service account to use for a pipeline run.
 
-[AutoML tabular regression pipelines using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
+[AutoML tabular regression pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
 
 Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular regression model.
 
@@ -110,7 +110,7 @@ The steps performed include:
 
 
 
-[Model upload, predict, and evaluate using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb)
+[Model upload, predict, and evaluate using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb)
 
 Learn how to evaluate a custom model using a pipeline with components from `google_cloud_pipeline_components` and a custom pipeline component you build.
 
@@ -122,7 +122,7 @@ The steps performed include:
 - Compare the evaluation metrics to a threshold.
 
 
-[Pipeline control structures using the KFP SDK](official/pipelines/control_flow_kfp.ipynb)
+[Pipeline control structures using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)
 
 Learn how to use the KFP SDK to build pipelines that use loops and conditionals, including nested examples.
 
@@ -133,7 +133,7 @@ The steps performed include:
 - Compile the KFP pipeline.
 - Execute the KFP pipeline using `Vertex AI Pipelines`
 
-[Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML](official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb)
+[Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb)
 
 Learn how to build a Vertex AI pipeline and train a Random-forest model using Spark ML for loan-eligibility classification problem.
 
@@ -145,7 +145,7 @@ The steps performed include:
 *   Build a Vertex AI pipeline and run the training job.
 *   Use the Spark serving image in order to deploy a Spark model on Vertex AI Endpoint.
 
-[AutoML text classification pipelines using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
+[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
 
 Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.
 

notebooks/official/reduction_server/README.md

@@ -1,5 +1,5 @@
 
-[PyTorch distributed training with Vertex AI Reduction Server](official/reduction_server/pytorch_distributed_training_reduction_server.ipynb)
+[PyTorch distributed training with Vertex AI Reduction Server](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb)
 
 Learn how to create a Python source distribution with the training code and dependencies to use with a pre-built containers on Vertex AI.
 

notebooks/official/sdk/README.md

@@ -1,5 +1,5 @@
 
-[AutoML Video Classification Example](official/sdk/SDK_AutoML_Video_Classification.ipynb)
+[AutoML Video Classification Example](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/SDK_AutoML_Video_Classification.ipynb)
 
 The objective of this notebook is to build a AutoML Video Classification Model.
 
@@ -14,7 +14,7 @@ The steps performed include the following:
 - Perform batch prediction job on the model
 
 
-[Custom training using Python package, managed text dataset, and TF Serving container](official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb)
+[Custom training using Python package, managed text dataset, and TF Serving container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb)
 
 Learn how to create a Custom Model using Custom Python Package Training and you learn how to serve the model using TensorFlow-Serving Container for online prediction.
 

notebooks/official/structured_data/README.md

@@ -1,5 +1,5 @@
 
-[BQML and AutoML - Experimenting with Vertex AI](official/structured_data/rapid_prototyping_bqml_automl.ipynb)
+[BQML and AutoML - Experimenting with Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb)
 
 Learn how to use `Vertex AI Predictions` for rapid prototyping a model.
 

notebooks/official/tabnet/README.md

@@ -1,5 +1,5 @@
 
-[Vertex AI Explainations with TabNet models](official/tabnet/ai-explanations-tabnet-algorithm.ipynb)
+[Vertex AI Explainations with TabNet models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb)
 
 Learn how to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.
 
@@ -9,7 +9,7 @@ The steps performed are:
 * Visualize and understand the feature importance based on the masks output.
 * Clean up the resource created by this tutorial.
 
-[Vertex AI TabNet](official/tabnet/tabnet_vertex_tutorial.ipynb)
+[Vertex AI TabNet](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb)
 
 Learn how to run TabNet model on Vertex AI.
 

notebooks/official/tabular_workflows/README.md

@@ -1,5 +1,5 @@
 
-[TabNet Pipeline](official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb)
+[TabNet Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb)
 
 Learn how to create two classification models using Vertex AI TabNet Tabular Workflows.
 
@@ -10,7 +10,7 @@ The steps performed include:
 
 
 
-[Wide & Deep Pipeline](official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb)
+[Wide & Deep Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb)
 
 Learn how to create two classification models using Vertex AI Wide & Deep Tabular Workflows.
 

notebooks/official/tensorboard/README.md

@@ -1,5 +1,5 @@
 
-[Vertex AI TensorBoard custom training with prebuilt container](official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb)
+[Vertex AI TensorBoard custom training with prebuilt container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb)
 
 Learn how to create a custom training job using prebuilt containers, and monitor your training process on Vertex AI TensorBoard in near real time.
 
@@ -10,7 +10,7 @@ The steps performed include:
 * Package and upload your training code to Google Cloud Storage.
 * Create & launch your custom training job with Tensorboard enabled for near real time monitorning.
 
-[Vertex AI TensorBoard Custom Training with Custom Container](official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb)
+[Vertex AI TensorBoard Custom Training with Custom Container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb)
 
 Learn how to create a custom training job using custom containers, and monitor your training process on Vertex AI TensorBoard in near real time.
 

notebooks/official/training/README.md

@@ -1,5 +1,5 @@
 
-[PyTorch image classification multi-node distributed data parallel training on cpu using Vertex training with custom container](official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb)
+[PyTorch image classification multi-node distributed data parallel training on cpu using Vertex training with custom container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb)
 
 Learn how to create a distributed PyTorch training job using Vertex AI SDK for Python and custom containers.
 
@@ -11,7 +11,7 @@ The steps performed include:
 - Create a Vertex AI tensorboard instance to store your Vertex AI experiment
 - Run a Vertex AI SDK CustomContainerTrainingJob
 
-[PyTorch Image Classification Multi-Node Distributed Data Parallel Training on GPU using Vertex AI Training with Custom Container](official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb)
+[PyTorch Image Classification Multi-Node Distributed Data Parallel Training on GPU using Vertex AI Training with Custom Container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb)
 
 Learn how to create a distributed PyTorch training job using Vertex AI SDK for Python and custom containers.
 

notebooks/official/vizier/README.md

@@ -1,5 +1,5 @@
 
-[Optimizing multiple objectives with Vertex AI Vizier](official/vizier/gapic-vizier-multi-objective-optimization.ipynb)
+[Optimizing multiple objectives with Vertex AI Vizier](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb)
 
 Learn how to use `Vertex AI Vizier` to optimize a multi-objective study.